Humanized C3 Mouse: A Novel Accelerated Model of C3 Glomerulopathy.

BACKGROUND: C3 glomerulopathy (C3G) is characterized by the alternative-pathway (AP) hyperactivation induced by nephritic factors or complement gene mutations. Mice deficient in complement factor H (CFH) are a classic C3G model, with kidney disease that requires several months to progress to renal failure. Novel C3G models can further contribute to understanding the mechanism behind this disease and developing therapeutic approaches. METHODS: A novel, rapidly progressing, severe, murine model of C3G was developed by replacing the mouse C3 gene with the human C3 homolog using VelociGene technology. Functional, histologic, molecular, and pharmacologic assays characterize the presentation of renal disease and enable useful pharmacologic interventions in the humanized C3 (C3hu/hu) mice. RESULTS: The C3hu/hu mice exhibit increased morbidity early in life and die by about 5-6 months of age. The C3hu/hu mice display elevated biomarkers of kidney dysfunction, glomerulosclerosis, C3/C5b-9 deposition, and reduced circulating C3 compared with wild-type mice. Administration of a C5-blocking mAb improved survival rate and offered functional and histopathologic benefits. Blockade of AP activation by anti-C3b or CFB mAbs also extended survival and preserved kidney function. CONCLUSIONS: The C3hu/hu mice are a useful model for C3G because they share many pathologic features consistent with the human disease. The C3G phenotype in C3hu/hu mice may originate from a dysregulated interaction of human C3 protein with multiple mouse complement proteins, leading to unregulated C3 activation via AP. The accelerated disease course in C3hu/hu mice may further enable preclinical studies to assess and validate new therapeutics for C3G.

Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma.

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient's disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.g., ixazomib, lenalidomide, dexamethasone (IRd) vs lenalidomide, dexamethasone (Rd). Using TOURMALINE trial data, we trained and internally validated our model on newly diagnosed myeloma patients (N = 703) and externally validated it on relapsed and refractory myeloma patients (N = 720). Our model achieved superior performance to a risk model based on the multiple myeloma international staging system (ISS) (p < 0.001, Bonferroni corrected) and comparable performance to survival models trained separately on each task, but unable to forecast biomarkers. Our approach outperformed state-of-the-art deep learning models, tailored towards forecasting, on predicting key disease biomarkers (p < 0.001, Bonferroni corrected). Finally, leveraging our model's capacity to estimate individual-level treatment effects, we found that patients with IgA kappa myeloma appear to benefit the most from IRd. Our study suggests that a holistic assessment of a patient's myeloma course is possible, potentially serving as the foundation for a personalized decision support system.

Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis.

Multiple myeloma is a plasma cell malignancy almost always preceded by precursor conditions, but low tumor burden of these early stages has hindered the study of their molecular programs through bulk sequencing technologies. Here, we generate and analyze single cell RNA-sequencing of plasma cells from 26 patients at varying disease stages and 9 healthy donors. In silico dissection and comparison of normal and transformed plasma cells from the same bone marrow biopsy enables discovery of patient-specific transcriptional changes. Using Non-Negative Matrix Factorization, we discover 15 gene expression signatures which represent transcriptional modules relevant to myeloma biology, and identify a signature that is uniformly lost in abnormal cells across disease stages. Finally, we demonstrate that tumors contain heterogeneous subpopulations expressing distinct transcriptional patterns. Our findings characterize transcriptomic alterations present at the earliest stages of myeloma, providing insight into the molecular underpinnings of disease initiation.